Chunk-level reordering of source language sentences with automatically learned rules for statistical machine translation

  • Authors:
  • Yuqi Zhang;Richard Zens;Hermann Ney

  • Affiliations:
  • RWTH Aachen University, Aachen, Germany;RWTH Aachen University, Aachen, Germany;RWTH Aachen University, Aachen, Germany

  • Venue:
  • SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we describe a source-side reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0.5%--1.8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level.